Latest news with #ARMs
Business Wire
4 days ago
- Business
- Business Wire
Ai2 Unveils MolmoAct, a New Class of AI Model That Reasons in 3D Space
SEATTLE--(BUSINESS WIRE)-- Ai2 (The Allen Institute for AI) today announced the release of MolmoAct 7B, a breakthrough embodied AI model that brings the intelligence of state of the art AI models into the physical world. Instead of reasoning through language and converting that into movement, MolmoAct actually sees its surroundings, understands the relationships between space, movement and time, and plans its movements accordingly. It does this by generating visual reasoning tokens that transform 2D image inputs into 3D spatial plans—enabling robots to navigate the physical world with greater intelligence and control. 'With MolmoAct, we're laying the groundwork for a new era of AI—one that can reason and navigate the world in ways more aligned with human thinking, and collaborate with us safely and effectively.' -Ali Farhadi, CEO of Ai2 Share While spatial reasoning isn't new, most modern systems rely on closed, end-to-end architectures trained on massive proprietary datasets. These models are difficult to reproduce, expensive to scale, and often operate as opaque black boxes. MolmoAct offers a fundamentally different approach: it's trained entirely on open data, designed for transparency, and built for real-world generalization. Its step-by-step visual reasoning traces make it easy to preview what a robot plans to do and intuitively steer its behavior in real time as conditions change. 'Embodied AI needs a new foundation that prioritizes reasoning, transparency, and openness,' said Ali Farhadi, CEO of Ai2. 'With MolmoAct, we're not just releasing a model; we're laying the groundwork for a new era of AI, bringing the intelligence of powerful AI models into the physical world. It's a step toward AI that can reason and navigate the world in ways that are more aligned with how humans do — and collaborate with us safely and effectively.' A New Class of Model: Action Reasoning MolmoAct is the first in a new category of AI model Ai2 is calling an Action Reasoning Model (ARM), a model that interprets high-level natural language instructions and reasons through a sequence of physical actions to carry them out in the real world. Unlike traditional end-to-end robotics models that treat tasks as a single, opaque step, ARMs interpret high-level instructions and break them down into a transparent chain of spatially grounded decisions: 3D-aware perception: grounding the robot's understanding of its environment using depth and spatial context Visual waypoint planning: outlining a step-by-step task trajectory in image space Action decoding: converting the plan into precise, robot-specific control commands This layered reasoning enables MolmoAct to interpret commands like 'Sort this trash pile' not as a single step, but as a structured series of sub-tasks: recognize the scene, group objects by type, grasp them one by one, and repeat. Built to Generalize and Trained to Scale MolmoAct 7B, the first in its model family, was trained on a curated dataset of about 12,000 'robot episodes' from real-world environments, such as kitchens and bedrooms. These demonstrations were transformed into robot-reasoning sequences that expose how complex instructions map to grounded, goal-directed actions. Along with the model, we're releasing the MolmoAct post-training dataset containing ~12,000 distinct 'robot episodes.' Ai2 researchers spent months curating videos of robots performing actions in diverse household settings, from arranging pillows on a living room couch to putting away laundry in a bedroom. Despite its strong performance, MolmoAct was trained with striking efficiency. It required just 18 million samples, pretraining on 256 NVIDIA H100 GPUs for about 24 hours, and fine-tuning on 64 GPUs for only two more. In contrast, many commercial models require hundreds of millions of samples and far more compute. Yet MolmoAct outperforms many of these systems on key benchmarks—including a 71.9% success rate on SimPLER—demonstrating that high-quality data and thoughtful design can outperform models trained with far more data and compute. Understandable AI You Can Build On Unlike most robotics models, which operate as opaque systems, MolmoAct was built for transparency. Users can preview the model's planned movements before execution, with motion trajectories overlaid on camera images. These plans can be adjusted using natural language or quick sketching corrections on a touchscreen—providing fine-grained control and enhancing safety in real-world environments like homes, hospitals, and warehouses. True to Ai2's mission, MolmoAct is fully open-source and reproducible. Ai2 is releasing everything needed to build, run, and extend the model: training pipelines, pre- and post-training datasets, model checkpoints, and evaluation benchmarks. MolmoAct sets a new standard for what embodied AI should look like—safe, interpretable, adaptable, and truly open. Ai2 will continue expanding its testing across both simulated and real-world environments, with the goal of enabling more capable and collaborative AI systems. Download the model and model artifacts – including training checkpoints and evals – from Ai2's Hugging Face repository. About Ai2 Ai2 is a Seattle-based non-profit AI research institute with the mission of building breakthrough AI to solve the world's biggest problems. Founded in 2014 by the late Paul G. Allen, Ai2 develops foundational AI research and innovative new applications that deliver real-world impact through large-scale open models, open data, robotics, conservation platforms, and more. Ai2 champions true openness through initiatives like OLMo, the world's first truly open language model framework, Molmo, a family of open state-of-the-art multimodal AI models, and Tulu, the first application of fully open post-training recipes to the largest open-weight models. These solutions empower researchers, engineers, and tech leaders to participate in the creation of state-of-the-art AI and to directly benefit from the many ways it can advance critical fields like medicine, scientific research, climate science, and conservation efforts. For more information, visit
Time of India
27-04-2025
- Business
- Time of India
Bengaluru Urban holds its ground as Karnataka weaves silk growth story
Bengaluru: Despite rapid urbanisation, Bengaluru Urban has reinforced its presence in Karnataka's silk arena, producing 174 tonnes of raw silk in 2024-25 and securing the 12th spot among top silk-producing districts in the state. Overall, Karnataka's silk production continues to grow steadily, reaching 13,276 tonnes this year — an increase of nearly 800 tonnes compared to the previous year. Mandya leads the way with a production of 3,540 tonnes and together with Kolar, Chikkaballapur, and Ramanagara, it contributes over 10,000 tonnes, accounting for the lion's share of the state's total production and ensuring Karnataka remains the top silk producing state in the country. You Can Also Check: Bengaluru AQI | Weather in Bengaluru | Bank Holidays in Bengaluru | Public Holidays in Bengaluru MB Rajesh Gowda, commissioner, sericulture development, attributed the sector's growth to several supportive measures. He told TOI: "The govt and Central Silk Board (CSB) has helped increase installations of automatic reeling machines (ARMs) and sericulturists get a good price for cocoons." by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Trade Bitcoin & Ethereum – No Wallet Needed! IC Markets Start Now Undo Highlighting past challenges and the turnaround, Gowda said, "Earlier, farmers sustained huge losses due to diseased silkworms. But now, they get disease-free layings. The price too has been good over the past few years. The govt continuous to encourage sericulturists by providing subsidies, including on construction of buildings to house silkworms. All these efforts have helped Karnataka remain the country's leading silk-producing state." A senior officer from the sericulture department said mulberry cultivation continues in pockets of Bengaluru Urban, especially in Anekal, Doddaballapur, and Hoskote taluks, helping sustain sericulture as a major income source for many farmers. Bengaluru Rural district also contributed 660 tonnes of raw silk this year. "Sericulturists get cocoons from silkworms in 20 to 25 days," he said. "The price of cocoons has been good for the past three years, so farmers who abandoned sericulture few years ago have resumed cultivation." In 2024-25, the department distributed 52 ARMs and aims to distribute 60 more this year. SC & ST reelers receive a 90% subsidy on the cost of machines, while others get 75% subsidy. ARMs help extract silk threads from cocoons efficiently, boosting supply of raw silk yarn and ensuring stable prices for farmers. "We plan to establish ARMs in the northern districts of the state to encourage silk production there," Gowda said. Sericulture department data shows mulberry is cultivated on nearly 1.2 lakh hectares in Karnataka, producing 93,624 tonnes of cocoon in 2024-25. The state's raw silk production rose from 11,823 tonnes in 2022-23 to 12,463 tonnes in 2023-24 and now stands at 13,278 tonnes. Data from the ministry of textiles reaffirm Karnataka's dominance in silk production nationally, with Andhra Pradesh and Assam ranking second and third, respectively.
